Nature Machine Intelligence
28 items across the graph · 28 news stories — tagged with Nature Machine Intelligence.
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Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift
Nature Machine Intelligence, Published online: 18 August 2026; doi:10.1038/s42256-026-01285-x Pan et al. present CoxRTL, a recalibrated transfer learning strategy that leverages external cohorts to improve survival prediction under covariate shift when target training data are limited and deployment outcomes are unavai…
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Agentic AI and cybersecurity, the story so far
Nature Machine Intelligence, Published online: 18 August 2026; doi:10.1038/s42256-026-01301-0 Frontier large language models (LLMs) have rapidly developed from helpful coding assistants to highly capable cybersecurity systems. After several security incidents in the past few mont…
Read full story →Machine learning of artistic fingerprints in jazz
Nature Machine Intelligence, Published online: 17 August 2026; doi:10.1038/s42256-026-01279-9 Cheston et al. develop a machine learning pipeline that identifies 20 iconic jazz pianists from audio recordings with up to 94% accuracy, revealing how melody, harmony, rhythm and dynami…
Read full story →Towards general auditory intelligence for machine listening and speaking
Nature Machine Intelligence, Published online: 14 August 2026; doi:10.1038/s42256-026-01281-1 Wang et al. summarize advances in machine listening and speaking, speech-based interaction, and audio–visual understanding.
Read full story →Towards principled knowledge editing methods for large language model reasoning
Nature Machine Intelligence, Published online: 14 August 2026; doi:10.1038/s42256-026-01276-y Chen et al. explore limitations of current knowledge editing techniques in large language models and propose three promising research directions that respect the complexity of knowledge…
Read full story →Capable language models can outgrow the benefits of collaboration
Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01268-y A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects…
Read full story →Thinking and rethinking data AI readiness
Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01288-8 Training machine learning models on high-quality biological datasets can quickly produce abundant results. But as datasets often evolve over time, further work is required to update models…
Read full story →Neural sampling from cognitive maps enables goal-directed imagination and planning
Nature Machine Intelligence, Published online: 21 July 2026; doi:10.1038/s42256-026-01254-4 Lin et al. introduce a brain-inspired generative model that provides two key features of intelligence: planning and problem-solving. It uses cognitive maps, stochastic computing and compos…
Read full story →A neural network model of free recall learns multiple memory strategies
Nature Machine Intelligence, Published online: 20 July 2026; doi:10.1038/s42256-026-01274-0 Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an in…
Read full story →Enabling local neural operators to perform equation-free system-level analysis
Nature Machine Intelligence, Published online: 15 July 2026; doi:10.1038/s42256-026-01265-1 Moving beyond brute-force simulations, local neural operators—combined with equation-free methods and Krylov subspace techniques—enable system-level stability and bifurcation analysis of c…
Read full story →A unifying framework from neural superposition to sparse interpretable codes
Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.
Read full story →Realigning AI technology towards the Sustainable Development Goals
Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01271-3 Realigning AI technology towards the Sustainable Development Goals
Read full story →A manifesto for Sustainability Robotics
Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01260-6 Song et al. propose Sustainability Robotics as a new discipline to overcome fragmentation and enhance societal and environmental impact. They define three guiding principles, alongside two…
Read full story →Towards shared embodied intelligence in humanoid robots through optimization, development and testing of the human-aware ergoCub robot
Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01272-2 Sartore et al. present ergoCub, a humanoid robot that prioritizes human safety at hardware and motion levels. Using a shared embodied intelligence framework, design and control are jointly…
Read full story →Four questions for AI-ready biological data
Nature Machine Intelligence, Published online: 10 July 2026; doi:10.1038/s42256-026-01270-4 The push to make biological data ‘AI-ready’ is accelerating worldwide. But to understand what AI-ready means for biological data, there are several questions to answer.
Read full story →Guiding generative models to uncover diverse and novel crystals via reinforcement learning
Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4 Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.
Read full story →Principled approaches for extending neural architectures to function spaces for operator learning
Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that gener…
Read full story →Empowering biomedical evidence exploration and synthesis with deep knowledge graph research
Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0 Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and e…
Read full story →Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1
Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01264-2 Liu and colleagues introduce HelixFold-S1, a guided sampling strategy for biomolecular complex structure prediction that targets high-probability interaction regions. The method achieves h…
Read full story →An agentic artificially intelligent X-ray scientist
Nature Machine Intelligence, Published online: 01 July 2026; doi:10.1038/s42256-026-01261-5 Chen et al. demonstrate an AI X-ray scientist that autonomously aligns single crystals at a real synchrotron beamline, showing how large language models can enable adaptive closed-loop exp…
Read full story →Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language
Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01250-8 Xiong et al. introduce ConfSeq, a molecular conformation description language that enables language models to perform three-dimensional molecular modelling tasks, including conformer predi…
Read full story →From virtual experiments to biomedical insight with synthetic data
Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01244-6 Synthetic datasets are becoming crucial for the development of biomedical machine learning models. Victoriano et al. discuss the persistent simulation-to-reality gap that limits how well s…
Read full story →Towards AI-augmented decision making in psychiatry
Nature Machine Intelligence, Published online: 12 June 2026; doi:10.1038/s42256-026-01256-2 Psychiatric disorders are heterogeneous, and care depends on interpreting unstructured longitudinal narratives, creating variability that hinders standardization. A study now shows that a…
Read full story →Algorithm–hardware co-design of neuromorphic networks with dual memory pathways
Nature Machine Intelligence, Published online: 16 June 2026; doi:10.1038/s42256-026-01255-3 Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficien…
Read full story →Autonomous navigation of intelligent microrobotic swarms in unknown environments
Nature Machine Intelligence, Published online: 22 June 2026; doi:10.1038/s42256-026-01252-6 An, Luo, Zhang and colleagues present Turbo, a transformer-based reinforcement learning framework that enables simulation-to-real transfer for autonomous navigation and obstacle avoidance…
Read full story →A dexterous soft hand exoskeleton restores intentional grasping in individuals with severe hand impairment
Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01263-3 Nassour, Berberich and colleagues present a soft robotic hand exoskeleton that restores grasping ability in individuals with severe hand paralysis, enabling meaningful tasks such as feedin…
Read full story →Solutions, challenges and rising tensions in AI and mathematics
Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01269-x Recent breakthroughs in mathematical research show that AI is transforming the field at a remarkable pace. In an open letter published this month, an international group of mathematicians…
Read full story →Data-driven surrogates of rational design enable antimicrobial peptide optimization
Nature Machine Intelligence, Published online: 25 June 2026; doi:10.1038/s42256-026-01258-0 Rising pathogen drug resistance makes next-generation antimicrobial peptides a global priority. Generative AI accelerates discovery by rapidly proposing new peptides with high therapeutic…
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